Note ~ Due to the length of this article, only excerpts are provided in this page. To read the entire article, click here.
1 INTRODUCTION
The Limits to Growth (LtG) is the name of a study conducted in the late 1960s for the Club of Rome. A group of researchers at the Massachusetts Institute of Technology developed a computer model that simulated some of the world's most important material variables, such as population, food production, resource use, and environmental impact. A total of 12 scenarios were presented in 1972 in the first book of the same name (Meadows et al., 1972). The scenarios cover the period from 1900 to 2100. The authors emphasize that the scenarios are not predictions. Rather, they are intended to illustrate the complex interrelationships within a dynamic system based on exponential growth.
The first and probably best known scenario is called the “standard run” or “business as usual” (BAU), which shows an exponential growth dynamic of the system and leads to the overshoot and collapse mode triggered by the depletion of non-renewable resources. The other scenarios describe changes in the parameterization of the model and assumptions about technological and societal developments. In the scenario called business as usual 2 (BAU2), twice as many initial non-renewable resources (NRI) were assumed and recycling technologies were implemented. These changes result in a different trajectory for each variable, but do not change the overshoot and collapse mode. The collapse in this case is caused by excessive pollution (Meadows et al., 1972).
The scenario comprehensive technology (CT) assumes a very broad application of technological solutions. Thus, the pollution rate is greatly reduced, crop yields on agricultural land are greatly increased, and resource efficiency is set above all historical values (Herrington, 2021). The basic dynamics in this scenario are different from those mentioned above. The industrial variables as well as the food production still show exponential growth, but the population growth slows down and reaches a plateau from the middle of the analyzed time period. In this scenario, the collapse is postponed to the end of the time period under consideration, but there are some steep downward slopes at the end (Meadows et al., 1972). The stabilized world (SW) scenario models a future state in which world population, industrial production, and resource consumption reach a steady state, resulting in a sustainable balance between human society and the environment. It is the only scenario in which the model variables are not in an overshoot and collapse mode (Meadows et al., 2005).
2 METHODS
Today, extensive online data sources and powerful computers allow for more accurate and faster modeling and data comparison than was possible in 1972. This paper takes advantage of this by using a Python implementation of Vanwynsberghe (2021) as a base model and updating it to the latest version of World3. The model data (MD) is then compared to the empirical data (ED) using a statistical measure to determine the difference. To minimize the divergence, selected parameters are varied and the results are iteratively improved.
To keep reading, go to the original article.
3 RESULTS
Using this approach, a set of parameters is calculated, which are shown and explained in this section. Sensitivity analyses were also performed by changing the initial parameters and the weighting.
To keep reading, go to the original article.
Figure 3. Recalibration23, improved run compared to BAU. The underlying data for this figure are available in Table S3 of Supporting Information S1.
Click on the image to enlarge.
|
To keep reading, go to the original article.
4 DISCUSSION
The discussion is divided into three parts. The first part analyzes the results from 1900 to the present. The second part points out the limitations. Finally, the third part gives an outlook on future trends.
To keep reading, go to the original article.
5 CONCLUSION
In this paper, the World3 model of the LtG study has been recalibrated to reflect the behavior of empirical data over the last 50 years. For this purpose, 35 parameters of the model were selected and optimized for a selected set of eight different empirical data sets that most closely reflect historical developments. An algorithm was developed to minimize the aggregated NRMSD [net root mean square deviation] between the model data and the empirical data using an iterative method. A new scenario with the improved parameter set was presented. Of the original 1972 LtG scenarios, the BAU scenario matches these parameters and the evolution of the variables most closely. Like the BAU scenario of the LtG publication, the new scenario Recalibration23 reflects the overshoot and collapse mode due to resource scarcity. However, the peaks of certain variables are raised and partially shifted into the future.
REFERENCES
To read the list of references, go to the original article.
|
ABOUT THE AUTHORS
Arjuna Nebel, Alexander Kling, Ruben Willamowski, Tim Schell ~ Faculty of Process Engineering, Energy, and Mechanical Systems, Cologne Institute for Renewable Energy, TH Köln – University of Applied Sciences, Köln, Germany.
|
![[groups_small]](groups_small.gif)